BNPmix is an R package for Bayesian nonparametric multivariate density estima-tion, clustering, and regression, using Pitman-Yor mixture models, a flexible and robust generalization of the popular class of Dirichlet process mixture models. A variety of model specifications and state-of-the-art posterior samplers are implemented. In order to achieve computational efficiency, all sampling methods are written in C++ and seamless integrated into R by means of the Rcpp and RcppArmadillo packages. BNPmix exploits the ggplot2 capabilities and implements a series of generic functions to plot and print summaries of posterior densities and induced clustering of the data
A class of nonparametric hierarchical mixtures is considered for Bayesian density estimation. This ...
This paper presents the R package MitISEM (mixture of t by importance sampling weighted expectation ...
Summary There has been increasing interest in applying Bayesian nonparametric methods in large sampl...
BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and r...
International audienceRobust statistical data modelling under potential model mis-specification ofte...
Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling ...
msBP is an R package that implements a new method to perform Bayesian multiscale nonparametric infer...
Normalized compound random measures are flexible nonparametric priors for related distributions. We ...
Normalized compound random measures are flexible nonparametric priors for related distributions. We ...
We investigate the class of σ-stable Poisson–Kingman random probability measures (RPMs) in the cont...
In recent years, there has been increasing interest in Bayesian nonparametric methods for high-dimen...
This paper documents a software package, VBmix, which implements in R several probabilistic mixture ...
<p>We investigate the class of σ-stable Poisson–Kingman random probability measures (RPMs) in the co...
PReMiuM is a recently developed R package for Bayesian clustering using a Dirich-let process mixture...
In the Bayesian nonparametric family, Dirichlet Process (DP) is a prior distribution that is able to...
A class of nonparametric hierarchical mixtures is considered for Bayesian density estimation. This ...
This paper presents the R package MitISEM (mixture of t by importance sampling weighted expectation ...
Summary There has been increasing interest in applying Bayesian nonparametric methods in large sampl...
BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and r...
International audienceRobust statistical data modelling under potential model mis-specification ofte...
Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling ...
msBP is an R package that implements a new method to perform Bayesian multiscale nonparametric infer...
Normalized compound random measures are flexible nonparametric priors for related distributions. We ...
Normalized compound random measures are flexible nonparametric priors for related distributions. We ...
We investigate the class of σ-stable Poisson–Kingman random probability measures (RPMs) in the cont...
In recent years, there has been increasing interest in Bayesian nonparametric methods for high-dimen...
This paper documents a software package, VBmix, which implements in R several probabilistic mixture ...
<p>We investigate the class of σ-stable Poisson–Kingman random probability measures (RPMs) in the co...
PReMiuM is a recently developed R package for Bayesian clustering using a Dirich-let process mixture...
In the Bayesian nonparametric family, Dirichlet Process (DP) is a prior distribution that is able to...
A class of nonparametric hierarchical mixtures is considered for Bayesian density estimation. This ...
This paper presents the R package MitISEM (mixture of t by importance sampling weighted expectation ...
Summary There has been increasing interest in applying Bayesian nonparametric methods in large sampl...